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Rapid Word Learning Through Meta In-Context Learning

Created by
  • Haebom

Author

Wentao Wang, Guangyuan Jiang, Tal Linzen, Brenden M. Lake

Outline

Inspired by the human ability to rapidly learn new words from a small number of examples and use them flexibly across diverse contexts, this paper presents Minnow (Meta-training for In-context Learning of Words), a novel method for improving word learning capabilities within a few attempts of a language model. Minnow trains a language model to generate examples of new words using special placeholder tokens. Repeated training on a variety of new words develops general word learning capabilities. Experimental results demonstrate that training a language model from scratch with Minnow using child language data achieves word learning capabilities comparable to those of large-scale language models (LLMs) pre-trained with much more data within a few attempts. Furthermore, fine-tuning Minnow on a pre-trained LLM improves the ability to segment new words, identify their syntactic categories, and generate new usage examples and definitions based on a few contextual examples. This highlights Minnow's data efficiency and its potential to enhance language model performance in word learning tasks.

Takeaways, Limitations

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Takeaways:
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We present the effectiveness of the Minnow method, which can train a language model with strong word learning capabilities using only a small amount of data.
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We demonstrate that Minnow can be effectively utilized to improve the word learning ability of pre-trained large-scale language models.
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Minnow can be used to achieve performance improvements across a variety of word learning tasks, including segmenting new words, identifying syntactic categories, and generating new exemplars and definitions.
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Limitations:
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This paper relies on specific datasets and evaluation metrics to evaluate Minnow's performance, and further research is needed to understand its generalization performance across diverse datasets and contexts.
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Analysis of Minnow's computational cost and scalability is lacking. Further research is needed to verify its effectiveness when applied to large-scale datasets.
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Minnow's learning performance on words with multiple meanings or complex semantics has not been clearly demonstrated. Further research is needed to evaluate Minnow's generalization performance on these words.
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